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Activity Number: 417 - Recent advancement on life time data analysis
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: Lifetime Data Science Section
Abstract #318573
Title: Efficient Estimation of Semiparametric Transformation Model with Interval-Censored Data in Two-Phase Cohort Studies
Author(s): Fei Gao* and Kwun Chuen Gary Chan
Companies: Fred Hutchinson Cancer Research Center and University of Washington
Keywords: Case-cohort design; EM algorithm; Interval-censoring; Kernel estimation; Nonparametric likelihood; Semiparametric efficiency

Interval sampling and two-phase sampling have both been advocated for studying rare failure outcome. However, they are often studied separately in the statistical literature. We consider efficient estimation of interval-censored data collected in a general two-phase sampling design using a localized nonparametric likelihood. An expectation maximization algorithm is proposed by exploiting multiple layers of data augmentation that handle transformation function, interval censoring, and two-phase sampling structure simultaneously. We study the asymptotic properties of the estimators and conduct inference using profile likelihood. We illustrate the performance of the proposed estimator by simulations and an HIV vaccine trial.

Authors who are presenting talks have a * after their name.

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